The Role of Conditional Likelihoods in Latent Variable Modeling

Anders Skrondal () and Sophia Rabe-Hesketh
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Anders Skrondal: Norwegian Institute of Public Health

Psychometrika, 2022, vol. 87, issue 3, No 1, 799-834

Abstract: Abstract In psychometrics, the canonical use of conditional likelihoods is for the Rasch model in measurement. Whilst not disputing the utility of conditional likelihoods in measurement, we examine a broader class of problems in psychometrics that can be addressed via conditional likelihoods. Specifically, we consider cluster-level endogeneity where the standard assumption that observed explanatory variables are independent from latent variables is violated. Here, “cluster” refers to the entity characterized by latent variables or random effects, such as individuals in measurement models or schools in multilevel models and “unit” refers to the elementary entity such as an item in measurement. Cluster-level endogeneity problems can arise in a number of settings, including unobserved confounding of causal effects, measurement error, retrospective sampling, informative cluster sizes, missing data, and heteroskedasticity. Severely inconsistent estimation can result if these challenges are ignored.

Keywords: Endogeneity; Fixed effects; Random effects; Conditional maximum likelihood; Marginal maximum likelihood; Unobserved confounding; Measurement error; Retrospective sampling; Informative cluster size; Missing data; Heteroskedasticity (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11336-021-09816-8

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